
import colorsys

from PIL import Image  # type: ignore

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			x
  __magic_name__								=			y
  for step in range(A_   ):  # noqa: B007
    __magic_name__								=			a * a - b * b + x
    __magic_name__								=			2 * a * b + y
    __magic_name__								=			a_new

    # divergence happens for all complex number with an absolute value
    # greater than 4
    if a * a + b * b > 4:
      break
  return step / (max_step - 1)

def a__       (			A_   ):






  '''simple docstring'''





  if distance == 1:
    return (0, 0, 0)
  else:
    return (255, 255, 255)

def a__       (			A_   ):






  '''simple docstring'''





  if distance == 1:
    return (0, 0, 0)
  else:
    return tuple(round(i * 255   ) for i in colorsys.hsv_to_rgb(A_,	1,	1   )   )

def a__       (			A_ = 800,	A_ = 600,	A_ = -0.6,	A_ = 0,	A_ = 3.2,	A_ = 50,	A_ = True,	):






  '''simple docstring'''





  __magic_name__								=			Image.new("""RGB""",	(image_width, image_height)   )
  __magic_name__								=			img.load()

  # loop through the image-coordinates
  for image_x in range(A_   ):
    for image_y in range(A_   ):
      # determine the figure-coordinates based on the image-coordinates
      __magic_name__								=			figure_width / image_width * image_height
      __magic_name__								=			figure_center_x + (image_x / image_width - 0.5) * figure_width
      __magic_name__								=			figure_center_y + (image_y / image_height - 0.5) * figure_height

      __magic_name__								=			get_distance(A_,	A_,	A_   )

      # color the corresponding pixel based on the selected coloring-function
      if use_distance_color_coding:
        __magic_name__								=			get_color_coded_rgb(A_   )
      else:
        __magic_name__								=			get_black_and_white_rgb(A_   )

  return img


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # colored version, full figure
    __lowerCAmelCase     :	Dict       			=  get_image()

    # uncomment for colored version, different section, zoomed in
    # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
    # figure_width = 0.8)

    # uncomment for black and white version, full figure
    # img = get_image(use_distance_color_coding = False)

    # uncomment to save the image
    # img.save("mandelbrot.png")

    img.show()



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	int       			=  {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""trocr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }
  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=5_0265     ,				UpperCamelCase__     :					int=1024     ,				UpperCamelCase__     :					int=12     ,				UpperCamelCase__     :					List[Any]=16     ,				UpperCamelCase__     :					Union[str, Any]=4096     ,				UpperCamelCase__     :					List[Any]="gelu"     ,				UpperCamelCase__     :					Optional[int]=512     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					int=0.0     ,				UpperCamelCase__     :					str=0.0     ,				UpperCamelCase__     :					Optional[int]=2     ,				UpperCamelCase__     :					List[str]=0.02     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					str=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					List[str]=2     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			activation_function
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			init_std
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			use_cache
    __magic_name__								=			scale_embedding
    __magic_name__								=			use_learned_position_embeddings
    __magic_name__								=			layernorm_embedding

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

